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Munich Personal RePEc Archive

International Monetary Arrangements

for the 21st Century—Which Way?

Hossain, Monzur

2009

Online at https://mpra.ub.uni-muenchen.de/24866/

MPRA Paper No. 24866, posted 10 Sep 2010 17:30 UTC

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Bangladesh Institute of Development Studies (BIDS) E�17 Agargaon, Sher�e�Bangla Nagar, Dhaka 1207

Bangladesh E�mail: [email protected]

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This paper examines some competing views on currency regime choice by applying the

dynamic multi�state Markov (MSM) model to the regime transitions of 166 countries from 1980

to 1999. The findings suggest that the bipolar view is valid only in the long run and for a reason

quite different from what the proponents had imagined, namely, economic development rather

than crisis�driven exits. The estimated steady�state probabilities even predict that a unipolar fixed

exchange rate system could emerge in the long run. Despite some divergence, both �� and

�� regime data corroborate the key findings.

�� : The bipolar view, Regime transition, Crisis, Developmental stage, Markov model

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I would like to thank Kenichi Ohno for his valuable inputs in this paper. I also thank Fahad Khalil and Iftekhar Hasan for their helpful comments. The paper has benefitted from the comments of seminar participants of the Mid�West Finance Association seminar and seminar at GRIPS, Tokyo. The usual disclaimer applies.

2

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“…….contingent policy rules to hit explicit exchange rate targets will no longer be viable in the twenty�first century……[Countries]….will be forced to choose between floating exchange rates on the one hand and monetary unification on the other.” Eichengreen (1994), pp. 4�5.

The bipolar view, which became popular in the 1990s, considers all exchange rate regimes other

than completely fixed and freely floating as unsustainable1. Advocates of the bipolar view argue

that greater exposure to global capital markets makes intermediate exchange rate regimes such as

adjustable peg, crawling peg and crawling band susceptible to speculative attacks. Countries will

be forced to move to the corners, either to a fixed regime such as currency union, currency board

or dollarization, or to a freely floating regime (Eichengreen, 1994; Obstfeld and Rogoff, 1995;

Summers, 2000).�The bipolar view is also consistent with the� �%��(�� . This

doctrine states that a country must give up one of the three goals: exchange rate stability,

monetary independence and financial market integration, that is, a country cannot have all three

simultaneously.

The proponents of the bipolar view claim that an intermediate regime will be hollowed out

over time as a result of involuntary transitions, particularly for a crisis2. The view was rapidly

adopted by the financial establishments as the “new conventional wisdom” (Frankel, 2003) after

the Asian crisis in 1997�98 with a view to “reform financial architecture” so as to minimize the

frequency and severity of crises in the future. As a result, as Eichengreen (1994) anticipated, the

distribution of currency regimes will take a bipolar form in the 21st century. This paper,

therefore, aims to examine the validity of the claim in a thorough empirical investigation.

1 This view is variously called, “the corners hypothesis”, “the two poles view”, “hollowing out hypothesis” and “the vanishing middle”. 2 As in the currency crisis models, an abandonment of an intermediate regime (pegs) happens when countries with intermediate regime experience excessive domestic credit growth, overvalued exchange rates, or weak economic growth and high unemployment.

3

Do crises lead to the disappearance of the intermediate regime? Even though the

proponents of the bipolar view argue that crises produce an irreversible transition toward the

poles, various case studies show that transitions occur not just away from an intermediate

regime, but also toward it. Some transitions are hastily executed and some are highly deliberated,

with or without the occurrence of crises. For example, a number of Asian countries adopted

floating regimes during the time of crisis in 1997�98, but they reverted to a �� intermediate

regime after the crisis. Thailand and Korea are notable examples (Hernandez and Montiel, 2003).

On the other hand, the currency union was adopted voluntarily by European countries in 1999

with preceding preparations done in steps, such as the Exchange Rate Mechanism (ERM) of the

European Monetary System, and its predecessor, the Snake.

Therefore, both voluntary and involuntary transitions can happen toward the poles, which

seem contradictory with the claim of the proponents of the bipolar view. It is therefore asked in

this paper, is the bipolar view likely to be valid for crises, as mentioned by the proponents, or

could it be valid for different reasons? To answer this question, the dynamics of currency regime

transitions of 166 countries are studied for the period 1980�99 using both �� and ��

data!.

The paper examines whether the distribution of exchange rate regimes tends to converge to

the polar extremes and identify factors that increase the likelihood of convergence. To this end,

combined with transition intensities involving determinants of regime transitions under the

multi�state Markov (MSM) model, this paper sets a testable approach as follows. +��

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(�� ,�� .�� (�� ,��. Although this approach points to a weaker definition of the

3 Not all countries’ regimes are available for all dates.

4

bipolar view as it allows some transitions toward the intermediate regime, it is helpful in

exploring a broader scope of the view.

Two recent studies provide evidence against the bipolar view. Masson (2001) is the first

study which cast a light on the hollowing out hypothesis directly and showed that there are non�

zero transition probabilities to intermediate regimes, implying that the bipolar view is not correct

strictly. However, it did not study the causes. In another study, Masson and Ruge�Murcia

(2005) endogenize the exchange rate regime transitions to show that inflation and, to a lesser

extent, output growth and trade openness can explain regime transitions, but not necessarily

toward the bipolar direction.

Although significant contributions, it is not clear from these studies whether the view may

gain some validity from a long run perspective and for the reasons mentioned by its proponents,

the main one being crises. Our analysis departs from the above studies in two important ways.

First, while the strict definition of the view (i.e. no transition toward intermediate regime) is not

supported by data (Masson, 2001), this study proposes an alternative test involving a weaker

definition of the view by allowing some transitions toward the intermediate regime. The

proposed approach has made it possible to explore a broader scope of the view instead of its

narrow scope under the strict rule. Second, and more importantly, this study tests the view by

analyzing the effects of crisis and developmental stage directly on the regime transition

dynamics, which was not done in earlier studies.

The primary antecedents of choosing explanatory variables, “crisis” and “developmental

stage” are found in the literature of involuntary and voluntary regime choice models, such as the

currency crisis models and optimum currency area (OCA) models, respectively (see Masson and

Ruge�Murcia, 2005; Poirson, 2001). Developmental stage dummy is chosen to capture various

5

dynamics of development as well as to proxy capital integration since capital control dummy is

not found significant to regime transitions. Moreover, when relatively more developed countries

face crisis in an intermediate regime in the face of high capital mobility, they are likely to move

to the corner and sustain it as long as they have more developed financial institutions (Hossain,

2009a). But this may not be the case for relatively less developed countries because they often

revert to an intermediate regime from the corner after the crisis as they cannot comply with the

conditions required to sustain a corner regime. These dynamics have been investigated in this

paper, and within that context, it is found that the bipolar view is likely to be valid during later

stages of development.

To analyze the dynamics of regime transition with the effects of relevant explanatory

variables as a part of testing the hollowing out hypothesis, this study chooses to apply the multi�

state Markov (MSM) model (see Appendix II for details). Particularly, this model has been

chosen for some of its special features. First, as a statistical methodology, the MSM model has

been widely used in the biomedical sciences to analyze transition between different states of

diseases and death (an absorbing state) and it can predict the probability of death with the effect

of a disease through estimating survival (or hazard) probabilities4. The MSM model is well

suited since testing the hollowing out hypothesis requires an analysis of whether a polar extreme

becomes an absorbing state over time with transition from the intermediate regime. Second, a

continuous�time model is used because the duration of individual regime is a continuous time

variable, which provides estimates of transition intensities (the instantaneous rate of transition)

between exchange rate regimes as a non�linear function of the relevant explanatory variables

assuming that transition happens instantaneously. This is a useful assumption required for testing

the bipolar view as transition between currency regimes generally happens instantaneously with

4 For details, see Kay (1986), Islam (1994), Marshall and Jones (1995).

6

the effect of crisis or for other reasons. Finally, the model can even be used to predict the

expected time for hollowing out of intermediate regimes allowing for the effects of possible

explicit causes. These features of this model make it an adequate framework for testing the

hollowing out hypothesis5.

Moreover, the hidden Markov model (HMM), an extension of the MSM model with

misclassification, is introduced to increase the precision of the test of the bipolar view by

considering probabilities of misclassification of regimes, as we observe a substantial amount of

discrepancies between �� and �� regime classifications (as shown in Section 2).

To summarize the main results, this study finds that economic development significantly

increases the likelihood of transition from an intermediate regime to a fixed or a floating regime,

but crises do not. The analysis shows that less developed countries move from an intermediate

regime in the face of crisis but most of them revert to an intermediate regime due to crisis in

corner regimes, particularly in a floating regime. This rejects the claim that crises produce an

irreversible transition toward the poles. However, crisis can be a factor of short�term regime

dynamics, and hence, it cannot be an imperative to hollowing out of intermediate regime in the

long run.

What would be the long run distribution of currency regimes? This is the question that is

asked in the title of the paper. Since the current distribution of currency regimes may change in

the future in the face of capital mobility or changing status of development, we estimate steady�

state probabilities under the MSM to have an idea about the long run distribution of regimes. We

5The MSM differs from the Markov chain models used in earlier studies such as Masson (2001) and Masson and Ruge�Murcia

(2005) in terms of its properties to estimate survival (hazard) probabilities. The estimation of survival probabilities of intermediate regimes is crucial for this study as it is necessary to estimate the expected time of hollowing out of intermediate regime for explicit causes.

7

find from the estimates that there is high probability for relatively developed countries to adopt a

fixed regime in the long run.

The remainder of the article is divided into six sections. Section 2 discusses a number of

technical issues related to testing the bipolar view, such as the divergence between �� and ��

�� classifications and a snapshot of regime transitions over time. The empirical framework

and results of the study are discussed in Sections 3 and 4, respectively. The application of the

Hidden Markov Model to deal with the problem of regime misclassification and the prediction of

hollowing out of intermediate regimes are presented in the next two sections (Sections 5 and 6).

Section 7 concludes this paper.

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Explaining the choice of a regime appears to be difficult in the beginning since the

classification of a regime is problematic. Note that the IMF has traditionally offered a

classification which is “��”, that is, it is essentially based on what the countries report to the

IMF. As a result, it does not reflect the actual regime when countries diverge from their officially

announced regime for certain periods of time. This divergence potentially affects the analysis of

historical trends in exchange rate regimes. However, based on the available ��

classification data sets, Hossain (2009b) analyzes divergence as a stochastic process of learning

to a �� regime, and argues that it would not affect the long�run distribution of exchange rate

regimes.

To ensure the robustness of the empirical investigation of the bipolar view, this section

provides a discussion on discrepancies between �� and �� regime classifications in the

following subsection.

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8

In this section, the proportion of divergence is estimated between �� and two ��

regime classifications proposed by Bubula and Ötker Robe, (2002) (henceforth 123� ��

��) and Reinhart and Rogoff (2004) (henceforth RR)6. If the �� and ��

regimes do not match for a specific year, it is considered divergent and the percentage of

divergent cases is calculated with respect to the total number of observations.

Divergences are estimated within three broad categories of exchange rate regimes namely,

fixed, intermediate and floating. A ��0�� *�%� consists of hard pegs such as formal

dollarization, currency union and currency board arrangement; an ��%��*�%� consists

of all soft pegs such as conventional fixed pegs (basket pegs with published or secret weights,

single currency peg), crawling pegs, crawling bands and tightly managed floats; and a ��*�

��*�%� consists of managed floats without a predetermined range and free floats 7 . As the

definition of �� fixed regime is different from the above�mentioned one (because ��

fixed regime consists of conventional fixed regimes in addition to hard fixes)8, the percentage of

divergence from �� fixed to other �� regimes is not estimated. Hence the focus is

primarily on the divergence between intermediate and floating regimes.

The estimated percentages of divergence are reported in Table 1 (Panel A) for the BOR

classification (for the period 1990�99) and in Table 1 (Panel B) for the RR classification (for the

periods 1980�89 and 1990�99). According to BOR, around 12 percent of ��floaters in fact

operated intermediate regimes. But, according to the RR classification, this percentage is around

40 percent for the 1980s and 50 percent for the 1990s. This divergence has increased over time,

6 Some other notable �� classifications are, Ghosh, Gulde, Ostry and Wolf (1997); Levy�Yeyati and Sturzenegger (2002); Kaminsky and Schmukler (2001). /�� classifications are published by the IMF. 7 Note that the ‘freely falling’ category of the RR classification is lumped, in most cases, into the freely floating category, and sometimes it is placed under managed floating on the basis of other information gathered by Reinhart and Rogoff (2004). 8 Until 1999, countries were obliged to report their exchange rate regime to the IMF according to three categories: fixed (both hard fixes and conventional pegs), limited flexible regimes (other soft pegs) and freely floating. From 1999, the IMF asks countries to report their exchange rate regimes according to eight categories to overcome the limitations of old coarse classification system.

9

perhaps due to high exchange rate volatility, crises and episodes of high inflation. On the other

hand, the percentage of �� intermediate that was floating in practice has decreased in the

1990s (using BOR, 26 percent in the 1980s and 18 percent in the 1990s; and 14 percent

according to RR). These are perhaps the cases of inadvertent floating in the face of political or

financial crises. From the trend in both types of divergence, it may be argued that the fear of

floating has increased over time.

Another type of divergence, one that is particularly relevant to measurement error, is that

among �� classification schemes. The estimated proportion of the observed differences

between ��regimes is shown in Table 2. A total of 17 percent diverged cases are detected

where a majority of these are associated with intermediate and floating regimes. To understand

this divergence (or measurement error in �� classification), we have studied the case of

Malawi’s exchange rate regime classification. Malawi’s exchange rate regime was classified in

1990 as a �� peg, intermediate by BOR,� and floating by the RR classification. In fact,

Malawi’s official exchange rate was fixed to a basket of undisclosed currencies, which falls in

the intermediate regime category (see Reinhart and Rogoff, 2004). It appears that the RR

classification, relying on statistical tests, identifies the ‘nontransparent multiple currency basket’

as floating (or managed floating) because of frequent parity adjustments. However, it can be

classified as a basket (i.e. intermediate regime) if the researcher uses other information as in the

case of BOR. A basket peg is classified as fixed in �� classification according to the IMF’s

old coarse classification system (until 1999).

It is therefore necessary to recognize the possibility that the proportion of divergence is

slightly overestimated due to misclassification in ��regime classifications. To deal with the

10

probability of misclassification in �� classification systems, this study introduces the

Hidden Markov model in Section 5.

To conclude, the findings in this section show that the divergence between ��and ��

��regime classifications and among the �� classifications is not negligible. Thus, the ��

�� and two �� classifications are used in the analysis for robustness of the results.

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This subsection investigates the validity of the bipolar view in a snapshot. The percentages

of alternative exchange rate regimes adopted by countries in 1980, 1990 and 1999 are shown in

Figure 1. According to the �� classifications, the percentage of countries in intermediate

regimes remained almost the same in 1980 and in 1990, but it had declined in 1999. This finding

is consistent with Fischer (2001)9. However, the ��classification shows that the percentage

of countries in intermediate regimes has slightly increased over time.

Next, the percentage distribution of regimes between 1990 and 1999 according to the

stages of development (developing, emerging and developed) is examined in Figure 2. This is to

examine the implication of the stages of development on the choice of regimes. The definitions

of developed and emerging countries follow those of the Morgan Stanley Capital International

(MSCI) index and Emerging Markets Bond Index Plus (EMBI+) of J.P. Morgan (see Table A1 in

Appendix I). The rest are then defined as developing countries. Figure 2 shows that the

percentage of countries operating intermediate regimes is slowly declining over time irrespective

of the stage of development. Only the �� trend shows that the popularity of intermediate

regimes among developing countries is not declining.

9 Fischer (2001) observes that economies open to international capital flows have been and are in the process of moving away from intermediate regime, some towards harder pegs, more towards systems with greater exchange rate flexibility.

11

In summary, the snapshot analysis shows that intermediate regimes have been gradually

hollowing out from the 1980s, but more rapidly in the 1990s. The stage of development appears

somewhat irrelevant for the bipolar choice of regimes. However, it is not possible to understand

clearly the dynamics of regime transition from the snapshot or any static analysis. Because,

within the period covered, some countries shifted toward corner regimes while some shifted from

the corner regimes to intermediate regimes. The snapshot analysis ignores this short�to�medium

term dynamics and also causes of transitions are not known from the analysis. Therefore, it is

necessary to analyze the dynamics of regime transition to have a clear idea about the bipolar

view�� The subsequent sections of this paper will, therefore, deal with dynamic models of

exchange rate regime transitions.

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In this section, the basic analytical framework is discussed. The empirical framework for

testing the bipolar view is based on the multi�state Markov model (the model is discussed in

Appendix II). A diagram for displaying the transitions (λij) among the regimes, fixed (1),

intermediate (2) and floating (3) with the coefficients of covariates (β��) is shown in Figure 3.

This figure shows that transitions can take place from fixed to intermediate (λ12), fixed to

floating (λ13), intermediate to fixed (λ21), intermediate to floating (λ23), floating to fixed (λ31) and

floating to intermediate (λ32) regimes. The model is analyzed under this framework because no

absorbing state is found in the currency regime transition pattern for the period under

consideration.

To identify the factors that may facilitate transitions toward the bipolar direction, variables

suggested by the competing paradigms of regime choice can be considered as explanatory

variables. The focus of this study is primarily on the variables that are suggested by the

12

proponents of the bipolar view. These proponents argue that capital integration will make

intermediate regimes susceptible to speculative attacks and countries will therefore be forced to

adopt a corner regime. Hence, according to them, capital integration and crises are the potential

reasons for transition toward the bipolar direction. Since capital integration is not found

significant in this study (see Table 3), developmental stage is considered, because emerging and

developed economies have stronger links to global capital markets than developing economies.

Moreover, stages of development could be deemed as an important factor for regime transition as

macroeconomic fundamentals and financial sectors may behave differently in different stages of

development for different currency regimes. This study also investigates the effects of some

economic fundamentals on transitions.

With the effect of the aforementioned variables, the bipolar view is likely to be valid if the

rate of transition from intermediate to fixed (λ21) is significantly higher than the rate of reverse

transition (λ12), i.e. if λ21/λ12 > 1 and if the rate of transition from intermediate to float (λ23) is

significantly higher than the rate of reverse transition (λ32), i.e. if i.e. if λ23/λ32 > 1. Support for

either of the condition by data would imply a partial support to the bipolar view for the period

under consideration.

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The regime transition dynamics are analyzed by calculating the duration of an individual

regime in years from the 1980s and 1990s10. The dependent variable is regime classification,

which consists of fixed = 1, intermediate = 2 and floating = 3. A larger value of the dependent

variable indicates that a more flexible regime is likely to be adopted for the country in the period

under consideration.

10 The duration of a regime is calculated by how many years (it is in most cases fractional value) a country continued with a specific currency regime. This gives rise to a continuous time MSM model.

13

Note that the transition between regimes can happen either voluntarily or involuntarily.

For example, generally speaking, if transition happens as a result of economic development or

capital mobility, it may be viewed as voluntary. On the other hand, if transition happens in a

crisis, it may be viewed as involuntary. So the multi�state Markov model of exchange rate

regime transitions involves explanatory variables representing both voluntary and involuntary

elements.

The dynamics of regime transitions are explored in two stages. First, following the

traditional approach, the effects of some macroeconomic fundamentals implied by the OCA and

currency crisis models are assessed on the transitions. Generally, OCA models explain voluntary

transitions while crisis models explain involuntary transitions. From the OCA perspective,

literature suggests that while trade openness, the existence of a dominant trading partner, labor

mobility and nominal flexibility explain a move toward a fixed regime, economic development,

diversification of production and exports, and size of the economy explain a move toward a

floating exchange rate regime (Poirson, 2001; Von Hagen and Zhou, 2005). On the other hand,

as an involuntary choice model, the currency crisis models suggest a large number of

endogenous variables such as inflation rate, growth rate of GDP, unemployment rate, fiscal

deficit, level of reserves, growth of domestic credit etc. as the determinants of exchange rate

regime choice. Although there is no consensus on the positive relationship between these

macroeconomic fundamentals and the regime choice in existing literature (see Baxter and

Stockman, 1989; Juhn and Mauro, 2002), this analysis is revived under the MSM model in order

to shed additional insights into the choice of a polar regime. Second, transition intensities are

estimated allowing for the effects of the stage of development (developing = 1, emerging = 2,

developed = 3) and the role of currency crises (crisis = 1, no crisis = 0).

14

The crisis dummy is taken from Glick and Hutchison (2001) and other macroeconomic

fundamentals such as terms of trade, openness, inflation, broad money growth and per capita

GDP growth are taken from the International Financial Statistics (IFS) of the International

Monetary Fund (IMF).

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First we examine the transition dynamics considering explanatory variables suggested by

OCA and crises literature. We consider only four variables, such as trade openness, inflation, per

capita GDP and capital control (1 = control, 0 = open) as explanatory variables and do not

consider other potential variables suggested by literature in order to avoid collinearity problem in

themodel. The estimates of parameters are reported in Table 3 for the 1990s.

Table 3 shows that trade openness and high inflation increase the probability of a flexible

regime. This finding is consistent with many studies including Masson and Ruge�Murcia (2005).

However, per capita GDP growth, a proxy for economic development, is not found to be

significant to explain regime transitions. Traditionally, per capita GDP growth is used as a proxy

of economic development, which is a rough indicator of economic development. The coefficient

on capital control dummy is not significant on regime transition, indicating that the argument of

the proponents of the bipolar view that capital integration may lead countries to abandon the

intermediate regime is not supported by data. Therefore, rather than these macroeconomic

fundamentals, exogenous structural features of an economy, such as developmental stage and

crisis are used to explain regime transition dynamics in the following sub�section.

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The dynamics of currency regime transition towards fixed and floating corner is examined

by estimating the transition intensity rates λ̂ 21, λ̂ 12, λ̂ 23 and λ̂ 32� in Panel A of Tables 4 and 5

15

with the effect of developmental stage and crisis. Panel B of the tables 4 and 5 gives estimates of

the coefficients of explanatory variables on regime transitions. The results are reported in Table

4 for the 1980s and in Table 5 for the 1990s.

Table 5 (panel A) shows that the rate of transition from intermediate to fixed regime�� λ̂ ��

is statistically significant and higher than the rate of reverse transition, λ̂ ��. Therefore, the

“fixed corner” is satisfied in the 1990s (the insignificance of λ̂ ��for �� data implies that

very few transitions occurred from hard fixes to intermediate regimes, but λ̂ ��is significant in

�� data because some conventional pegs, which are classified as fixed in ��

classification, gave way to intermediate regimes). But, in the 1980s, as Table 4 shows, only ��

�� data satisfy the fixed corner. On the other hand, the estimated rate of transition from

intermediate to floating regime is lower than the rate of reverse transition, λ̂ �� λ̂ ��implying

that the “floating corner” is not satisfied by data in both the 1980s and 1990s� Both estimates,

λ̂ ��and� λ̂ �� are statistically significant, indicating that intermediate regimes are less likely to

give way to floating regimes. These estimates also indicate that there are some transitions toward

the intermediate regime from the polar extreme, which is not consistent with the predictions of

the proponents of the bipolar view.

Now consider the coefficients of the explanatory variables (βij) on regime transitions from

intermediate to fixed regime. The coefficient of developmental stage is negative and significant

(according to �� data, Table 4) in the 1980s while it is positive and significant in the 1990s

on the transition from intermediate to fixed regime (all three data sets corroborate this, see Table

5). This implies that although less developed countries were involved with such transitions in the

11 The rates of transition between fixed and intermediate regime are almost the same in BOR ��classification.

16

1980s, the situation has changed in the 1990s as more developed countries adopted fixed

regimes. Some examples of this trend are the creation of the European Monetary Union in 1999

as well as the adoption of currency boards in Argentina (1991) and Bulgaria (1997). On the other

hand, the effect of crises is not significant on the observed transition from intermediate to fixed

regime during the 1980s. But in the 1990s, crisis has positive and significant effects on both

transitions and reverse transitions (both �� and RR classification data satisfy this finding),

indicating that crisis does not only produce transition toward a polar extreme. Therefore, the

estimates of base�line transition intensities and the coefficients of explanatory variables together

indicate that crisis may not be an important factor for the hollowing out of intermediate regime;

rather it can be considered a factor of short�term regime transition dynamics.

Next, considering the transition from intermediate to floating regimes, the effect of

developmental stage ( 23β̂ ) is mixed in the 1990s as it is positive and significant according to the

RR classification, positive and insignificant according to BOR, and negative and significant

according to �� classification. The opposite sign for β̂ 23 in �� and RR ��

classification indicates a discrepancy which means that certain less developed countries which

officially claim to be floaters are, in fact, not. However, the positive and significant effect of

developmental stage on the transition from intermediate to floating regime via the RR

classification suggests that relatively advanced countries tend to adopt floating regimes. But the

role of crisis on the transition from intermediate to floating regime is not strong as the

coefficients are only marginally significant. These evidences suggest that there is high likelihood

for hollowing out of intermediate regimes with the rise of the level of economic development

over time.

17

Finally, considering the transition from floating to intermediate regime, the coefficient of

the stage of development is negative and statistically significant (according to both BOR and ��

�� data), suggesting that less developed countries (indicated by negative sign of the coefficient,

β̂ 32) were involved in such transition. Although the effect of crisis is insignificant on the

transition in the 1980s, a robust positive and significant effect (all three data set corroborate this)

of crisis in the 1990s implies that floating exchange rate regime is not a viable option for less

developed countries as they often experience crises in it. This runs counter to the widely held

opinion that intermediate regimes are the most susceptible to crises.

A noteworthy finding is that less developed countries often tend to switch to a more rigid

regime at the advent of a crisis, implying that crisis cannot be an imperative toward hollowing

out of intermediate regimes. This finding is also contrary to the predictions of the so�called first

generation model (Krugman, 1979) and second generation model (Obstfeld, 1996) of currency

crises 12 . Perhaps, the countries’ tendency to adopt a fixed regime at the time of crisis is

associated with the state of vulnerability of their financial sectors.

It should also be noted that despite a considerable amount of divergence among regime

classifications (as shown in Section 2), the key results do not substantially vary. This indicates

that the divergence characterized largely by short�term regime dynamics does not matter for

long�term regime trends.

�

To sum up, estimates of transition intensities suggest that the “fixed corner” is satisfied,

but the “floating corner” is not satisfied for the period under consideration, which rejects the

bipolar view even under the weaker definition given in this study. However, by analyzing the

12

In these models, crisis is usually resolved through a forced exit from the peg followed by a floating exchange rate regime normally characterized with exchange rate overshooting.

18

causes of transitions, it is found that the bipolar view will likely to be valid during later stages of

development.When countries reach a high level of development, which is also correlated, ��

��, with high capital integration, they tend to switch to either a fixed or a floating regime and

sustain it. Intermediate regimes remain viable for less developed countries, at least for the short

run. The empirical findings reveal that support for the bipolar view is relatively weak in the

1980s compared to the 1990s, indicating a decade�dependency in the regime transitions process.

It is worth noting, however, that the 1990s was marked by higher levels of economic

development as many countries entered into the emerging category, increased capital market

integration and global shocks that make intermediate regimes unsustainable to some extent. If

this pattern continues, the validity of the bipolar view will be more pronounced in the foreseeable

future.

*�+��

In this section the Hidden Markov model is applied to the �� regime classification

data sets to obtain estimates of transition intensities controlling for the probability of

misclassification. In the Hidden Markov model, the true regimes (or states) of the Markov chain

are not observed. The observed regimes are governed by some probability distribution (the

emission distribution) conditional on the unobserved regimes. For example, the observed states

Oi are generated conditionally on the true states Si according to a misclassification matrix E =

(ers)n×n, where ers = Pr (O(t) = s | S(t) = r), and is assumed to be independent of 13.

To investigate the effect of explanatory variables z(t) for the probability of

misclassification ers for each pair of states r and s, a logistic model can be used,

Log (ers /(1� ers)) = γTrs z(t).

13 The quality of a diagnostic test (sensitivity and specificity) is often measured by probabilities that the true and observed states are equal, Pr (Oi (t) = � | Si (t) = �).

19

Here a direct method is applied to calculate likelihoods in continuous time based on matrix

products, following the method developed by Macdonald and Zucchini (1997) and Guttorp

(1995).

The estimated misclassification probabilities are reported in Table 6. The sample period

considered for the HMM is 1990�99. The estimates in Table 6 show that the probability for

misclassifying a floating regime as an intermediate regime and vice�versa is around 0.04, which

is slightly higher than the probability of misclassifying the other regime.

The estimated transition intensities and coefficients of covariates are reported in Table 7.

The estimates from both BOR and RR classification show that the rate of transition from

intermediate to fixed regime is higher than the rate of reverse transition. On the other hand, the

rates of transition and reverse transition between intermediate and floating regimes are almost

the same. These results are robust as both �� classifications provide almost the same results

(after correcting for the probability of misclassification in it).

Turning to the effects of developmental stage and crisis on transitions, the effects of higher

levels of development (indicated by the positive sign of the coefficient of developmental stage)

and currency crisis are significant to the transition from intermediate to floating regime.

Nonetheless, crisis has significant effect also on the transitions from floating to intermediate

regime, and intermediate to fixed regime. This suggests that the effect of crisis is not very strong

for the reinforcement of the bipolar view. This set of findings confirms the commonly held belief

that less developed countries may find it advantageous to operate intermediate regimes while the

floating regime is better suited for more developed nations. Considering transition intensities

and the effect of developmental stage, it may be concluded the bipolar view is likely to be valid

in the long run.

20

The findings in this section using the HMM support the findings of the previous section

strongly.

,�-�� &��

Since the probabilities of adopting regimes may change over time as a result of various

events, such as capital mobility or crises in the future, current distribution of regimes may not be

the same as the long run one. Therefore, we ask: what would be the long run distribution of

exchange rate regimes? This section explores answer of this question by estimating the long�run

probability distribution (also known as invariant or steady�state distribution) of regimes under

the MSM. The estimates are reported in Table 8.

Table 8 (Panels A and B) shows that intermediate regimes will not be hollowing out

completely with or without crisis situation in the long run. However, according to ��data,

the long run probability of adopting an intermediate regime is found to be very low among

emerging and developed countries. Even there is a high probability of hollowing out of floating

regime with the emergence of a unipolar fixed exchange rate regime in later stages of

development.

The estimated steady�state probabilities are found to be low for both intermediate and

floating regimes, indicating that an intermediate regime is not viable for relatively advanced

countries. This is because intermediate regime does not bring much benefits such as anti�

inflationary or safe�guards against liability dollarization. Therefore, the findings in this section

are supportive of the bipolar view in the long run under a weaker definition given in the

Introduction section, and of the unipolar fixed system, in general.

.��

21

This study examines the bipolar view of currency regime choice in the context of crisis

and developmental stage. While the strict definition of the view (i.e. no transition toward

intermediate regime) is not supported by data, this study proposes an alternative test involving a

looser definition of the view by allowing some transitions toward the intermediate regime. The

proposed approach has made it possible to explore a broader scope of the view.

This study finds that economic development significantly increases the likelihood of

transition from an intermediate regime to a fixed or a floating regime, but crises do not. The

analysis shows that less developed countries move from an intermediate regime in the face of

crisis but most of them revert to an intermediate regime due to crises in corner regimes,

particularly in a floating regime. This rejects the claim of the proponents that the bipolar view

will be valid in the long run with irreversible transitions toward the poles for a crisis in

intermediate regimes. However, our findings suggest that the bipolar view is valid only in the

long run under a weaker definition and for a reason quite different from what the proponents

imagined, namely, economic development rather than crisis�driven exits.

The findings of this study suggest that an irreversible transition toward the poles occurs

both voluntarily and involuntarily when countries have achieved economic development and

integrated with global capital markets. The MSM model prediction shows that intermediate

regimes disappear more rapidly in the emerging and developed stages than the developing stage.

This finding is reasonable because when institutions are built and a good policy track is

established in the emerging and developed stages, an intermediate regime may not be suitable as

the economy will no longer require the benefits of an intermediate regime like anti�inflationary

credibility or safeguards against liability dollarization.

22

The estimates of long�run probabilities suggest that a unipolar fixed exchange rate system

might emerge in the long run as there is a high probability for adopting fixed regime among

relatively developed countries. This prediction comes from recent trends of countries adopting

currency union, currency boards and dollarization.

It is thus reasonable to argue that the looser definition that is taken in this study does not

contradict much with the spirit of the original view. Hence, if we take a nuanced view of the way

policies tend to work, a looser definition of the bipolar view might be appropriate.

The MSM model used in this study could not give an estimate of survivorship function of

intermediate regime due to the absence of an absorbing polar extreme in the data for the period

under consideration. Therefore, we could not predict the estimated time of hollowing out of

intermediate regime. However, the MSM model can be effectively applied if an absorbing

regime emerges over time, and more importantly, the model can be applied to many other similar

situations of economic analysis.

+��

Baxter, Marianne and Alan Stockman. 1989. “Business Cycles and the Exchange�Rate Regime:

Some International Evidence,” Journal of Monetary Economics�23, May, 377�400.

Bubula, Andrea and Inci Ötker�Robe. 2002. “The Evolution of Exchange Rate Regimes since

1990: Evidence from /��7�� Policies”, IMF Working Paper�� 8"##)�

Calvo, Guillermo A. and Carmen M. Reinhart. 2002. “Fear of Floating”, Quarterly Journal of

Economics) Vol. CXVII, Issue 2.

Cox, D. R. 1972. “Regression Models and Life Tables (With Discussion)”. Journal of Royal

Statistical Society, /, 34, pp. 187�220.

23

Eichengreen, Barry. 1994. “International Monetary Arrangements for the 21st Century”,

Brookings Institution-�Washington DC.

Fischer, S. 2001. “Exchange Rate Regimes: Is the Bipolar View Correct?” Distinguished Lecture

on Economics in Government, Journal of Economic Perspectives, Vol. 15, 2, pp. 3�24.

Frankel, Jeffrey A. (2003). “Experience of and Lessons from Exchange Rate Regimes in

Emerging Countries”, NBER Working Paper 10032, October.

Ghosh, Atish, Anne�Marie Gulde and Holger Wolf. 2002. Exchange Rate Regimes: Choices and

Consequences- MIT Press.

Ghosh, Atish, Anne�Marie Gulde, Jonathon D. Ostry and Holger Wolf. 1997. “Does the Nominal

Exchange Rate Regime Matter?” 91�3� :��.��*� �� 9�)� 5874 (Cambridge,

Massachusetts, National Bureau of Economic Research)

Glick, R., Michael Hutchison. 2001. Banking and Currency Crises in Financial Crises in

Emerging Markets. Ed. Reuven Glick, Ramon Moreno, and Mark Spiegel, 35�69.

Cambridge, Cambridge University Press.

Guttorp, P. 1995. Stochastic Modeling of Scientific Data) Chapman and Hall, London, 1995.

Hernandez, Leonardo, and Peter Montiel. 2003. “Post�Crisis Exchange Rate Policy in Five Asian

Countries: Filling in the “Hollow Middle”? Journal of the Japanese and International

Economies, Vol. 17, Issue 3, pp. 336�369)�

Islam, M. A. 1994. “Multistate Survival Models for Transitions and Reverse Transitions: An

Application to Contraceptive Use Data”, Journal of Royal Statistical Society, Series A,

157, Part 3, pp. 441�455.

24

Juhn, Grace S. and Paolo Mauro. 2002. “Long�Run Determinants of Exchange Rate Regimes: A

Simple Sensitivity Analysis”. IMF Working Paper 02/104)�

Kaminsky, G. and Sergio Schmukler. 2001. “Financial Excesses: Do Exchange Rate Regimes

Matter”? Mimeo, George Washington University, Washington, D.C.

Kalbfleisch, J. D. and J.F. Lawless, 1985, The Analysis of Panel Data under a Markov

Assumption. Journal of the American Statistical Association- 80(392): 863�871, 1985.

Kay, R., 1986, A Markov model for analyzing cancer markers and disease states in survival

studies. 1��%��, 42, pp. 855�865.

Krugman, P. 1979. “A Model of Balance�of�Payment Crises”, Journal of Money, Credit and

Banking-�11, 311�325.

Levy Yeyati, Eduardo and Federico Sturzenegger. 2002. “Classifying Exchange Rate Regimes:

Deeds Versus Words”, Universidad Torcuato di Tella)� available via the internet at:

www.utdt.edu/~fsturzen.

Macdonald, I. L. and W. Zucchini.1997. Hidden Markov and Other Models for Discrete�Valued

Time Series. Chapman and Hall, London.

Marshall, G. and R. H. Jones. 1995. “Multi�State Models and Diabetic Retinopathy”, Statistics in

Medicine, 14, 1995.

Masson, Paul. 2001. “Exchange Rate Regime Transitions”, Journal of Development Economics,

Vol. 64, 571�586.

Masson, Paul and Francisco J. Ruge�Murcia. 2005. “Explaining the Transition between

Exchange Rate Regimes”, Scandinavian Journal of Economics, Vol. 107, No. 2, pp. 261�

278, June 2005.�

25

Obstfeld, M. 1996. “Models of Currency Crises with Self�Fulfilling Features”, European

Economic Review-�6�- pp. 1037�1047.

Obstfeld, Maurice and Kenneth Rogoff. 1995. “The Mirage of Fixed Exchange Rates”, Journal

of Economic Perspectives-�Vol. 9, No. 4, pp. 73�96.

Poirson, H. 2001. “How do Countries Choose their Exchange Rate Regime”? IMF Working

Paper Wp/01/46.

Reinhart, Carmen M. and Kenneth Rogoff. 2004. “The Modern History of Exchange Rate

Arrangements: A Reinterpretation”, Quarterly Journal of Economics, Vol. 119, �0

February.

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American Economic Review-�Papers and Proceedings, Vol. 90, No. 2, pp. 1�16.

Von Hagen, J. and J. Zhou.2005. “The Choice of Exchange Rate Regime: An Empirical

Analysis for Transition Economies”, Economics of Transition- Vol 13 (4), pp. 679�703.

26

Table 1

Divergence between �� and �� exchange rate regime classifications

A. /��vs. BOR��(1990�1999)

Divergence from\to

/�� Total observations

/�� Fixed Intermediate Float Intermediate 1 336 77 414

(0.20%) (81.20%) (18.60%) (100%)

Float 1 109 315 425

(0.20%) (12.09%) (74.10%) (100%) �

�

B. /��vs. RR��(1980�1989, 1990�99) Divergence from\to

��/�� Total observations

/�� Fixed Intermediate Float

Intermediate

Float

1980�89 1990�99 1980�89 1990�99 1980�89 1990�99 1980�89 1990�99 0

(0.0%) 2

(0.50%) 219

(73.74%) 327

(85.20%) 78

(26.26%) 55

(14.30%) 297

(100%) 384

(100%) 0

(0.0%) 1

(0.30%) 52

(40.31%) 182

(50.30%) 77

(59.69) 174

(48.10%) 129

(100%) 357

(100%) 9��: 1. Estimates are based on total number of observations. Row percentages are reported.

2. Underline indicates consensus (consistent) regime.�

Table 2

Observed difference between BOR and RR �� classification (1990�1999) 33��

BOR ��

Fixed Intermediate Float Total observations

Fixed 225 4 2 231 (97.40%) (1.70%) (0.90%) (100%)

Intermediate 2 646 66 714 (0.30%) (90.50%) 9.20% (100%)

Float� 4 154 187 345 � (1.10%) (44.0%) (53.40%) (100%)�

Total observations

231 804 255 1290

9��: Row percentages are calculated. Total diverged cases are (off diagonal cases): 232 (17% of total cases). Underline indicates consensus (consistent) regime

27

Table 3 Estimated coefficients of macroeconomic fundamentals on regime transition in 1990�99

A. D��classification /�� 123�� 33��

Transition parameters

Trade openness

Per capita GDP growth

Inflation Capital control

Trade openness


Inflation Capital control

Trade openness


Inflation

Capital control

Fixed to inter. �0.48 (0.51) �0.82 (2.91)

1.82 (1.09)

0.27 (0.71)

�0.40 (0.51)

�0.30 (2.98)

�1.07 (3.40)

�0.18 (1.52)

�0.72 (0.55)*

�0.54 (2.98)

0.19 (1.81)

�0.05 (1.4)

Fixed to float �1.62 (0.60)*

0.18 (1.96)

1.86 (0.89)*

0.53 (0.72)

�1.08 (0.55)*

�0.30 (2.56)

0.77 (1.40)

�0.20 (1.47)

�1.27 (0.56)*

1.34 (0.88)*

1.70 (0.96)*

�0.06 (0.8)

Inter. to fixed 0.10 (0.54) �0.89 (8.3)

0.20 (4.03)

0.04 (0.80)

0.15 (0.33)

�0.97 (6.2)

�0.59 (2.09)

�0.11 (0.98)

0.0004 (0.37)

�1.60 (6.48)

�0.72 (1.88)

0.01 (0.62)

Inter. to float �1.77 (0.85)*

0.70 (6.01)

0.97 (1.33)

0.52 (0.51)

�1.21 (0.99)*

�0.76 (6.21)

0.61 (1.34)

0.66 (0.38)*

�0.94 (0.62)*

�0.57 (6.11)

�0.10 (1.39)

0.30 (0.77)

Float to fixed �0.002 (1.09)

�0.75 (6.1)

0.55 (5.05)

�0.06 (1.18)

0.17 (0.99)

�1.22 (5.7)

2.13 (1.81)

0.15 (1.38)

0.34 (0.82)

�2.84 (4.84)

0.64 (1.83)

0.03 (1.33)

Float to inter. �0.03 (0.49) �2.65 (2.47)

0.24 (1.16)

0.39 (0.38)

�0.08 (0.43)

0.14 (2.45)

0.66 (0.75)

0.42 (0.38)

0.17 (0.42)

1.67 (2.67)

�0.22 (0.99)

0.06 (0.59)

Notes: Log�likelihoods: RR: �232.70; BOR: �363.77; /��: �446.17 �Standard errors are in parentheses; ***,**,* indicates significance at 1, 5, 10 percent levels, respectively.

28

Table 4

A. Estimated rates of transition intensities, 1980�89

Transition intensities

RR �� (N = 1103)

/��

(N = 1091) Fixed to inter. (λ12) 0.04 (0.015)*** 0.03 (0.01)***

Fixed to float (λ13) 0.01 (0.012) 0.01 (0.006)

Inter. to fixed (λ21) 0.003 (0.005) 0.04 (0.02)** Inter. to float (λ23) 0.04 (0.005)*** 0.05 (0.015)*** Float to fixed (λ31) 0.001 (0.010) 0.01 (0.02)

Float to inter. (λ32) 0.05 (0.021)* 0.08 (0.03)***

B. Estimated coefficients of developmental stage and crisis on transition intensities

Developmental stage Currency crisis

/�� RR �� /�� RR ��

Fixed to inter. (β12) �0.30 (0.46) �0.26 (0.98) �0.91 (1.18) 2.33 (1.05)**

Fixed to float (β13) 0.58 (0.43) 0.71 (1.37) 1.48 (0.74)** �0.06 (5.15) Inter. to fixed (β21) �1.52 (0.54)** 0.51 (0.85) �0.004 (0.77) 0.114 (2.42)

Inter. to float (β23) �0.34 (0.44) �0.07 (0.27) 0.13 (0.89) 1.13 (0.47)*** Float to fixed (β31) 0.73 (1.38) 0.18 (2.3) �0.95 (2.91) 1.55 (3.53) Float to inter. (β32) �0.89 (0.50)* �0.16 (0.42) �0.14 (1.03) �0.08 (0.81)

Log�likelihood /��: �240.91; RR ��: �173.57 9��: Standard errors are in parentheses; ***,**,* indicates significance at 1, 5, 10 percent levels, respectively.

29

Table 5

A.� Estimated rates of transition intensities, 1990�99

Transition intensities /��

(N= 1573) BOR ��

(N=1616) RR �� (N= 1282)

Fixed to inter. (λ12) 0.028(0.008)*** 0.017 (.012) 0.017 (0.016) Fixed to float (λ13) 0.041 (0.009)*** 0.016 (.011) 0.03(0.016)***

Inter. to fixed (λ21) 0.058 (0.015)*** 0.013 (0.004)*** 0.026 (.007)** Inter. to float (λ23) 0.084 (0.017)*** 0.09 (0.011)*** 0.033 (0.007)***

Float to fixed (λ31) 0.02 (0.008)*** 0.006 (0.007) 0.03 (0.013)***

Float to inter. (λ32) 0.11 (0.019)*** 0.125 (0.022)*** 0.08 (0.026)***

B. Estimated coefficients of developmental stage and crisis on transition intensities, 1990�99


/�� BOR �� RR �� /�� BOR �� RR ��

Fixed to inter. (β12) 1.15 (0.38)*** �0.38 (1.33) 2.02 (0.73)*** 1.85 (0.84)** �0.24 (3.62) 3.77 (1.45)**

Fixed to float (β13) 0.33 (0.40) �0.60 (1.51) 0.41 (0.59) 1.43 (0.72)** 0.37 (2.68) 3.37 (0.77)***

Inter. to fixed (β21) 0.61 (0.25)** 1.12 (0.31)*** 1.06 (0.29)*** 1.11 (0.66)* 0.15 (1.04) 1.54 (0.92)*

Inter. to float (β23) �0.67 (0.32)** 0.02 (0.17) 0.55 (0.24)** 1.33 (0.66)* �0.21 (0.57) 0.78 (.60) Float to fixed (β31) 0.36 (0.42) 0.34 (0.71) �0.01 (0.45) 0.45 (1.05) 1.05 (1.52) 0.19 (1.72)

Float to inter. (β32) �0.32 (0.22)+ �0.53 (0.23)** �0.20 (0.29) 0.59 (0.019)*** 0.70 (0.36)** 1.14 (0.53)** Log�likelihood /��: �506.49; BOR ��: �490.52; RR ��: �286.31 9��: Standard errors are in parentheses. ***, **, * represent significance at 1, 5, 10 percent levels, respectively. Significance is tested by the Wald Test

Statistic Z = ;<)=�)>8< ββ �

30

Table 6

Estimated probabilities of errors in �� classification of regimes, 1990�99

Classification probabilities ��2(��,��*�%��

BOR ��classification Fixed Intermediate

Float

�

��4��

��*�%�

Fixed 0.99 (0.17) 0.00 (0.26) 0.003(0.14) Intermediat

e 0.03 (0.005) 0.93 (0.01) 0.04 (0.006)

Float 0.01 (0.003) 0.03 (0.01) 0.96 (0.02) 33��

�

��4��

��*�%�

Fixed 0.99 (0.25) 00 (0.25) 0.01 (0.25) Intermediat

e 0.04 (0.01) 0.92 (0.01) 0.04 (0.005)

Float 0.01 (0.001) 0.04 (0.01) 0.96 (0.025) Standard errors are in parentheses

Table 7

A. The estimates of transition intensities using the hidden Markov model, 1990�99

Estimated transition intensities

BOR �� RR ��

Fixed to inter. (λ12) 0.03 (0.03) 0.03 (3.50)

Fixed to float (λ13) 0.06 (0.02)*** 0.09 (0.01)***

Inter. to fixed (λ21) 0.05 (0.00)*** 0.06 (0.0)***

Inter. to float (λ23) 0.10 (0.01)*** 0.08 (0.0)***

Float to fixed (λ31) 0.04 (0.00)*** 0.06 (0.0)***

Float to inter. (λ32) 0.10 (0.01)*** 0.13 (0.0)***

B. Coefficients of developmental stage and currency crisis on transition intensities, 1990�99�


BOR �� RR �� BOR �� RR ��

Fixed to Inter. (β12) �3.42 (290.68) 0.63 (20.74) �1.58 (235.56) 1.50 (81.99)

Fixed to float (β13) 0.65 (5.55) 0.24 (3.84) 2.46 (11.38) �5.09 (255.96)

Inter. to fixed (β21) �0.28 (0.38) �0.32 (0.31) 1.99 (0.43)*** 0.99 (0.49)**

Inter. to float (β23) 0.35 (0.22) * 0.71 (0.22)***

1.89 (0.38)*** 0.66 (0.60)

Float to fixed (β31) 0.56 (0.537) 1.47 (1.59) 1.72 (0.96)* 3.41 (2.58)

Float to inter. (β32) �0.20 (0.397) �0.05 (0.39) 2.22 (0.67)*** 1.93 (0.67)***

Log�likelihood BOR ��: �839.96 RR ��: �646.48

***,**,* indicates significance at 1, 5, 10 percent levels, respectively.

31

Table 8

Steady�state probability distribution of exchange rate regimes

A. Without crisis

123�/�� 33�/��

Fixed

Intermediate

Float

Fixed

Intermediate

Float

Developing 0.13 0.52 0.36 0.33 0.57 0.10

Emerging 0.30 0.34 0.36 0.60 0.28 0.12

Developed 0.52 0.18 0.30 0.81 0.09 0.10

B. With crisis

123�/�� 33�/��

Fixed

Intermediate

Float

Fixed

Intermediate

Float

Developing 0.25 0.58 0.17 0.49 0.19 0.32

Emerging 0.52 0.33 0.15 0.81 0.09 0.10

Developed 0.76 0.14 0.10 0.95 0.02 0.02

32

��

��

��

��

��

��

��

��

��

��

!��

��

��

��

��

��

��

��

��

��

"#��

��

��

��

��

��

��

��

��

��

FIGURE 1

Percentage distribution of countries adopted alternative regimes in 1980, 1990 and 1999

33

RR de facto

0

2 0

4 0

6 0

8 0

1990 1999 1990 1999 1990 1999

Developing Emerging Developed

Per

cen

tag

e o

f co

un

trie

s

Peg Inter Float

BOR de facto

�

��

��

��

��

��

1990 1999 1990 1999 1990 1999


Per

cen

tag

e o

f co

un

trie

s

��

De jure

0

20

40

60

80

1990 1999 1990 1999 1990 1999


Per

cen

tag

e o

f co

un

trie

s

Peg Inter Float

FIGURE 2 Percentage distribution of countries adopted alternative regime in 1990 and 1999 according to

their developmental stage.

FIGURE 3 Transitions within the regimes of the MSM model

!Float

�

�Inter

λ��$β��

λ��$β��

λ��$β��%�� λ��$β��%

��λ��$β��

��

λ��$β��%

34

--"12�3�

Table A1 List of emerging and developed countries Emerging Countries (31) Developed Countries (22) Argentina, Brazil, Bulgaria, Chile, China, Colombia, Czech Republic, Ecuador, Egypt, Greece, Hungary, India, Indonesia, Israel, Jordan, Korea, Malaysia, Mexico, Morocco, Pakistan, Panama, Peru, Philippines, Poland, Qatar, Russian Federation, Sri Lanka, South Africa, Thailand, Turkey and Venezuela

Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Hong Kong, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, United Kingdom and United States.

Note: This list is based on the ?��*��>�� +�� (MSCI) index and �%��*��*�?��.��1��

+��0�� (EMBI+) of J.P. Morgan

--"12�3��

4��5��(�%��

The multi�state Markov model (MSM) is used in this paper to analyze the exchange rate

regime transition dynamics. Note that as the underlying economic structure such as parameters

and variance change over time, the nature of the optimal exchange rate regime is expected to

vary correspondingly, leading to an evolution of exchange rate regime. Therefore, the new

equilibrium position of the economy leads to an evolution of an exchange rate regime. This can

be true in a crisis situation when some countries change their regime and again return to the

previous regime after the crisis. This gives rise to a Markov model, which assumes that the

probability of transition to an alternative regime depends only on the current regime irrespective

of past history.� Masson (2001) argues that although the Markov assumption appears to be

somewhat restrictive, as a first approximation, it would seem to be an adequate framework for

examining exchange rate regime transitions as “it supposes that a typical currency will face the

same likelihood that some shock will push it to an alternative regime” (p. 573).�

35

The multi�state Markov model assumes continuous time Markov chain and explicitly takes

the duration of a regime into account. The model is specified as

(1) ( ) Z

ijijijez|t

βλπ = , (i, j = 1, 2, 3)

where λ�� represents baseline parameters (transition intensities), Z denotes the vector of

explanatory variables and β��denotes the coefficients of explanatory variables on the transition

from regime � to �. The transition intensities, λ��, can be defined as

(2) { }

�%��%��

��

�+→=

→�

|],(Prlim

0λ ��

Both time�dependent and time�invariant parameters can be estimated by the model. The

estimated parameters are obtained from the partial likelihood function and that the estimates are

less likely to suffer from omitted variable bias and multicollinearity. The Newton�Raphson

iterative optimization algorithm can be used for non�linear estimation of the coefficients.Since

this model assumes a continuous time Markov chain, it provides estimates of transition

intensities (instantaneous rate of transition). Using the Kolmogorov forward differential equation

(Eq. 3), transition probabilities can be estimated from transition intensities.

The MSM model considers that countries often make transitions and reverse transitions

among three exchange regimes― fixed (1), intermediate (2) and floating (3), either voluntarily

or involuntarily. It is assumed that there is no absorbing state (a state of no return) in the

exchange rate regime transition process. The transition intensity matrix is defined as,

( )( )

( )

+−

+−

+−

=

@ @"@ @"

@ @ " "

"@" "@"

λλλλλλλλ

λλλλ

Γ .

36

The elements of the matrix Γ , λij’s are defined in (2). Assume that the transition intensities

i.e. instantaneous rate of transition are independent of time and the intensities follow the

property ∑≠

−=��

�� λλ ; i, j = 1, 2, 3, that is, ��%��&��.

The relationship between the transition probability matrix -(t) and the transition intensity

matrix Γ can be established with the Kolmogorov forward differential equation

(3) Γ-6�7�

-6�7=

∂∂

,

where the (i,j)th element of the matrix -(t), �� (i,j = 1, 2, 3) represents the probability of

transition from state i to j in a time interval t. Thus the transition probability matrix -(t) can

be expressed as

=

@@@ @"

@ "

"@" ""

��

��

��

;=- .

4��8�(��

A general method for evaluating the likelihood for a multi�state Markov model in

continuous time, applicable to any form of transition is discussed here (for details, see

Kalbfleisch and Lawless, 1985; Kay, 1986). The likelihood is calculated from the transition

probability matrix -(t).The likelihood function is the product of all individual contributions and

the total contribution of an individual country to the likelihood function is the result of the

product of the contribution from each observed transition.

For a country ., the likelihood function is formulated as:

(4) [ ] [ ] [ ] [ ] [ ]

[ ] [ ] [ ] [ ] @@@ @ @

""@" ""

�

@@.

�

@ .

�

@".

�

@.

�

.

�

".

�

"@.

�

" .

.

�

"".

;&A=�;&A=�;&A=�;&A=�

;&A=�;&A=�;&A=�;&A=�;&A=�;=� ∏=θ

37

where θ = (λ, β), λ = (λij) and β= (βij) for �-� � = 1, 2, 3. The variable sij takes the value 1 if

transition occurs and 0 if otherwise. For example, if at time , a country is in state 1 (fixed

regime), at time �", the country can be in either of the states 1 (fixed), 2 (intermediate) or 3

(float). Therefore, s11+ s12 + s13 = 1, and so on. The log�likelihood function can be calculated by

taking the log of the likelihood function.

The maximum likelihood estimates of θ = (λ,β) can be obtained by maximizing the log

likelihood, and applying any of the iterative procedures such as the quasi�Newton algorithm or

Nelder�Mead simplex�based algorithm. The MSM model is estimated in this paper using the

“msm” package of + software.

international monetary arrangements for the 21st century ... · hossain, monzur 2009 online at...

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